ArticleFrontiers in cell and developmental biology2024
Predicting an opaque bubble layer during small-incision lenticule extraction surgery based on deep learning.
Article in Frontiers in cell and developmental biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Preoperative predictive factors for opaque bubble layer formation and area during small-incision lenticule extraction: predictive models based on machine learning.Eye and vision (London, England) · 2026Article
- Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aim: This study aimed to predict the formation of OBL during femtosecond laser SMILE surgery by employing deep learning technology. Methods: This was a cross-sectional, retrospective study conducted at a university hospital. Surgical videos were randomly divided into a training (3,271 patches, 73.64%), validation (704 patches, 15.85%), and internal verification set (467 patches, 10.51%). An artificial intelligence (AI) model was developed using a SENet-based residual regression deep neural network. Model performance was assessed using the mean absolute error ( Results: Four distinct types of deep neural network models were established. The modified deep residual neural network prediction model with channel attention built on the PyTorch framework demonstrated the best predictive performance. The predicted OBL area values correlated well with the Photoshop-based measurements ( Conclusion: We used a panoramic corneal image obtained before the SMILE laser scan to create a unique deep residual neural network prediction model to predict OBL formation during SMILE surgery. This model demonstrated exceptional predictive power, suggesting its clinical applicability across a broad field.
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Registered trials
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